We propose a new visual hierarchical representation paradigm for multi-object
tracking. It is more effective to discriminate between objects by attending to
objects' compositional visual regions and contrasting with the background
contextual information instead of sticking to only the semantic visual cue such
as bounding boxes. This compositional-semantic-contextual hierarchy is flexible
to be integrated in different appearance-based multi-object tracking methods.
We also propose an attention-based visual feature module to fuse the
hierarchical visual representations. The proposed method achieves
state-of-the-art accuracy and time efficiency among query-based methods on
multiple multi-object tracking benchmarks.

我们提出了一种新的多对象跟踪的视觉分层表示范式，并通过关注对象的组合性视觉区域和与背景的对比背景信息，不仅仅局限于语义可视线索（如边界框），而是更有效地区分对象。这种组合性 - 语义 - 上下文层次结构灵活地集成到不同的基于外观的多对象跟踪方法中。我们还提出了一种基于注意力的视觉特征模块来融合分层视觉表示。该方法在多个多对象跟踪基准中实现了最先进的准确性和时间效率。